Call CrossAttention __init__ with batch dim (#83)

Fixes T5 model inference

BUG=https://buganizer.corp.google.com/issues/352073514

ai_edge_torch/generative/examples/t5/t5_attention.py

@@ -27,6 +27,8 @@
 from ai_edge_torch.generative.layers.scaled_dot_product_attention import scaled_dot_product_attention  # NOQA
 from ai_edge_torch.generative.layers.scaled_dot_product_attention import scaled_dot_product_attention_with_hlfb  # NOQA
 
+BATCH_SIZE = 1
+
 
 class EncoderDecoderBlock(nn.Module):
 
@@ -44,6 +46,7 @@ def __init__(
 
     super().__init__()
     self.atten_func = T5Attention(
+        BATCH_SIZE,
         config.embedding_dim,
         config.attn_config,
         config.pre_attention_norm_config,
@@ -54,6 +57,7 @@ def __init__(
     # For a decoder, we add a cross attention.
     if config.is_decoder:
       self.cross_atten_func = T5Attention(
+          BATCH_SIZE,
           config.embedding_dim,
           config.attn_config,
           config.pre_attention_norm_config,
@@ -127,6 +131,7 @@ class T5Attention(CrossAttention):
 
   def __init__(
       self,
+      batch: int,
       dim: int,
       config: cfg.AttentionConfig,
       norm_config: cfg.NormalizationConfig,
@@ -144,7 +149,7 @@ def __init__(
       enable_hlfb (bool): whether hlfb is enabled or not.
       has_relative_attention_bias (bool): whether we compute relative bias.
     """
-    super().__init__(dim, dim, config, kv_cache_max, enable_hlfb)
+    super().__init__(batch, dim, dim, config, kv_cache_max, enable_hlfb)
     self.pre_atten_norm = builder.build_norm(dim, norm_config)
 
     self.has_relative_attention_bias = has_relative_attention_bias